Design Space

"Design space" refers to the range of possible configurations or parameters within a system, often explored to optimize performance or achieve specific goals. Current research focuses on efficiently navigating these spaces using various techniques, including gradient-based optimization, diffusion models, and evolutionary algorithms, often applied within specific model architectures like transformers, graph neural networks, and autoencoders. This exploration is crucial for advancing fields ranging from materials science and drug discovery (e.g., designing optimal protein structures or high-Tc superconductors) to artificial intelligence (e.g., improving the performance and explainability of LLMs and other AI models) and engineering design (e.g., optimizing the design of robots or aerospace vehicles). The ultimate aim is to develop more efficient and effective methods for exploring and exploiting design spaces across diverse scientific and engineering disciplines.